Experiment with Random Difficulty (results based on the confidence scale)
## Nombre de participants à l'expérimentation : 58## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
(pas nécessaire pour la mesure basée sur l’échelle de confiance)
{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #
{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #
{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #
Removing Outliers based on CONFIDENCE SCALE
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS NULL: 9b3ph38yc, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, bzrji9dqz, dyg7cga2o, dyg7cga2o, dyg7cga2o, e58u3sinl, kctu3te1y, kctu3te1y, m4ye7uz5h, qzh5zi9e8, tmxmxmwhi, tmxmxmwhi, urgv6o806, zp9bc59o5, zp9bc59o5, zv35u39vc"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 12"## [1] "Total number of outliers motor task: 11"## [1] "Total number of outliers perceptive task: 5"## [1] "Total number of outliers logical task: 6"Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1669.2 1690.0 -830.6 1661.2 1359
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8343 -0.7720 0.3062 0.7571 2.7501
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.4686 0.6846
## Number of obs: 1363, groups: IDjoueur, 47
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9982 0.1974 -5.057 4.27e-07 ***
## difficulty 2.8413 0.2301 12.346 < 2e-16 ***
## timeNorm -0.5530 0.2179 -2.538 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.549
## timeNorm -0.577 -0.022## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1363 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-0.96344
## 1st Qu.:-0.37670
## Median :-0.08364
## Mean :-0.00173
## 3rd Qu.: 0.21652
## Max. : 1.57591
## [1] "Intercept: -0.998 4.3e-07 ***"
## [1] "Difficulty: 2.84 5.1e-35 ***"
## [1] "Time: -0.553 0.011 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.26"
## [1] "Cross Val: 0.68"
## [1] "AIC: 1700"## 0% 25% 50% 75% 100%
## -1.57590870 -0.21652213 0.08364306 0.37669604 0.96343672
## 0% 25% 50% 75% 100%
## -1.57590870 -0.21652213 0.08364306 0.37669604 0.96343672
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1173.9 1195.1 -582.9 1165.9 1504
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2906 -0.3676 0.1154 0.3469 6.2131
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7411 0.8609
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1668 0.2640 -11.996 <2e-16 ***
## difficulty 8.1536 0.4159 19.606 <2e-16 ***
## timeNorm -0.4920 0.2782 -1.768 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.633
## timeNorm -0.505 -0.080## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol =
## 0.001, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol = 0.001, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1508
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.677712
## 1st Qu.:-0.448501
## Median : 0.077197
## Mean :-0.001156
## 3rd Qu.: 0.407249
## Max. : 1.510666
## [1] "Intercept: -3.17 3.7e-33 ***"
## [1] "Difficulty: 8.15 1.4e-85 ***"
## [1] "Time: -0.492 0.077 ."
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1200"## 0% 25% 50% 75% 100%
## -1.51066561 -0.40724859 -0.07719681 0.44850104 1.67771216
## 0% 25% 50% 75% 100%
## -1.51066561 -0.40724859 -0.07719681 0.44850104 1.67771216
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1444.5 1465.8 -718.2 1436.5 1533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0357 -0.4980 -0.1017 0.5004 5.0622
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.57 1.253
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9054 0.2628 -7.251 4.14e-13 ***
## difficulty 5.7562 0.3198 18.001 < 2e-16 ***
## timeNorm -1.9355 0.2564 -7.550 4.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.497
## timeNorm -0.376 -0.233## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1537 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8051717
## 1st Qu.:-0.7513212
## Median :-0.2064150
## Mean :-0.0003176
## 3rd Qu.: 0.7228639
## Max. : 3.1492300
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.37495, p-value = 0.7077
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04294701
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.91744, p-value = 0.3589
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1000199
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01388433Playing board games in general and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.99227, p-value = 0.3211
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1118
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.21922, p-value = 0.8265
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02354007
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.06919576Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 23 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.24953, p-value = 0.8029
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03718731## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3393258
##
## [1] "self.eff.on.level.s 0.34 0.013 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06648267Risk aversion and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3418, p-value = 0.1797
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1465938
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9118, p-value = 0.0559
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1966642
##
## [1] "risk.av.on.level.s 0.2 0.056 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1404273Age and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1372263## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9837, p-value = 0.04728
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1984774
##
## [1] "age.on.level.s 0.2 0.047 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1130316Sex and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0369, p-value = 0.04166
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2478106
##
## [1] "sexe.on.level.m -0.25 0.042 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.068275, p-value = 0.9456
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.007880754
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04451521
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 163, p-value = 0.04192
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.73654416 -0.04033621
## sample estimates:
## difference in location
## -0.3800085
##
## [1] "sexe.on.level.m.2 -0.38 0.042 * mean(A): 0.15 mean(B): -0.27"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 294, p-value = 0.9538
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4708587 0.5066973
## sample estimates:
## difference in location
## -0.02056307
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7753238 0.5708569
## sample estimates:
## difference in location
## -0.06017729CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.079 44 0.0014 **
## 2: 0.09375 0.120 54 6.5e-05 ***
## 3: 0.15625 0.110 55 0.00029 ***
## 4: 0.21875 0.120 53 1e-04 ***
## 5: 0.28125 0.100 53 0.00018 ***
## 6: 0.34375 0.094 50 0.00011 ***
## 7: 0.40625 0.074 53 0.033 *
## 8: 0.46875 0.011 53 0.63 :(
## 9: 0.53125 -0.014 50 0.6 :(
## 10: 0.59375 -0.058 54 0.0054 **
## 11: 0.65625 -0.078 52 0.00079 ***
## 12: 0.71875 -0.110 54 3.5e-05 ***
## 13: 0.78125 -0.160 53 3.2e-07 ***
## 14: 0.84375 -0.220 52 1.2e-08 ***
## 15: 0.90625 -0.230 55 3.8e-10 ***
## 16: 0.96875 -0.170 55 1.3e-09 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.0014 **
## 2: 54 6.5e-05 ***
## 3: 55 0.00029 ***
## 4: 53 1e-04 ***
## 5: 53 0.00018 ***
## 6: 50 0.00011 ***
## 7: 53 0.033 *
## 8: 53 0.63 :(
## 9: 50 0.6 :(
## 10: 54 0.0054 **
## 11: 52 0.00079 ***
## 12: 54 3.5e-05 ***
## 13: 53 3.2e-07 ***
## 14: 52 1.2e-08 ***
## 15: 55 3.8e-10 ***
## 16: 55 1.3e-09 ***
## [1] 52.5
## [1] 2.73
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0520 32 0.071 .
## 2: 0.09375 0.0560 33 0.067 .
## 3: 0.15625 0.0600 37 0.12 :(
## 4: 0.21875 0.0900 35 0.017 *
## 5: 0.28125 0.0770 34 0.026 *
## 6: 0.34375 0.0980 32 0.008 **
## 7: 0.40625 0.0940 35 0.016 *
## 8: 0.46875 0.0150 34 0.64 :(
## 9: 0.53125 0.0036 32 0.93 :(
## 10: 0.59375 -0.0600 38 0.074 .
## 11: 0.65625 -0.1100 32 0.0085 **
## 12: 0.71875 -0.1800 34 9.2e-05 ***
## 13: 0.78125 -0.1700 35 0.00056 ***
## 14: 0.84375 -0.2400 25 0.00024 ***
## 15: 0.90625 -0.2600 26 3.8e-05 ***
## 16: 0.96875 -0.1100 17 0.018 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.071 .
## 2: 33 0.067 .
## 3: 37 0.12 :(
## 4: 35 0.017 *
## 5: 34 0.026 *
## 6: 32 0.008 **
## 7: 35 0.016 *
## 8: 34 0.64 :(
## 9: 32 0.93 :(
## 10: 38 0.074 .
## 11: 32 0.0085 **
## 12: 34 9.2e-05 ***
## 13: 35 0.00056 ***
## 14: 25 0.00024 ***
## 15: 26 3.8e-05 ***
## 16: 17 0.018 *
## [1] 31.9
## [1] 5.23
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.120 27 0.0012 **
## 2: 0.09375 0.160 33 0.0011 **
## 3: 0.15625 0.110 32 0.013 *
## 4: 0.21875 0.081 35 0.011 *
## 5: 0.28125 0.120 32 0.04 *
## 6: 0.34375 0.076 32 0.034 *
## 7: 0.40625 0.044 34 0.55 :(
## 8: 0.46875 -0.011 31 0.88 :(
## 9: 0.53125 -0.015 34 0.78 :(
## 10: 0.59375 -0.048 32 0.29 :(
## 11: 0.65625 -0.150 36 0.0011 **
## 12: 0.71875 -0.069 35 0.021 *
## 13: 0.78125 -0.110 35 0.0013 **
## 14: 0.84375 -0.210 34 1.4e-05 ***
## 15: 0.90625 -0.220 31 6.1e-06 ***
## 16: 0.96875 -0.160 32 8.1e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 27 0.0012 **
## 2: 33 0.0011 **
## 3: 32 0.013 *
## 4: 35 0.011 *
## 5: 32 0.04 *
## 6: 32 0.034 *
## 7: 34 0.55 :(
## 8: 31 0.88 :(
## 9: 34 0.78 :(
## 10: 32 0.29 :(
## 11: 36 0.0011 **
## 12: 35 0.021 *
## 13: 35 0.0013 **
## 14: 34 1.4e-05 ***
## 15: 31 6.1e-06 ***
## 16: 32 8.1e-06 ***
## [1] 32.8
## [1] 2.2
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.140 9 0.15 :(
## 3: 0.15625 0.180 12 0.02 *
## 4: 0.21875 0.130 11 0.068 .
## 5: 0.28125 0.220 11 0.066 .
## 6: 0.34375 0.160 9 0.022 *
## 7: 0.40625 0.190 11 0.068 .
## 8: 0.46875 0.081 14 0.066 .
## 9: 0.53125 -0.031 13 0.4 :(
## 10: 0.59375 -0.094 14 0.073 .
## 11: 0.65625 0.044 12 0.36 :(
## 12: 0.71875 -0.094 15 0.082 .
## 13: 0.78125 -0.150 15 0.021 *
## 14: 0.84375 -0.200 17 0.0023 **
## 15: 0.90625 -0.210 18 0.00089 ***
## 16: 0.96875 -0.330 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.15 :(
## 2: 12 0.02 *
## 3: 11 0.068 .
## 4: 11 0.066 .
## 5: 9 0.022 *
## 6: 11 0.068 .
## 7: 14 0.066 .
## 8: 13 0.4 :(
## 9: 14 0.073 .
## 10: 12 0.36 :(
## 11: 15 0.082 .
## 12: 15 0.021 *
## 13: 17 0.0023 **
## 14: 18 0.00089 ***
## 15: 17 0.00031 ***
## [1] 13.2
## [1] 2.83## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0440 5 0.78 :(
## 3: 0.15625 -0.0730 19 0.13 :(
## 4: 0.21875 0.0190 35 0.65 :(
## 5: 0.28125 0.0350 40 0.36 :(
## 6: 0.34375 0.0900 40 0.018 *
## 7: 0.40625 0.0540 42 0.19 :(
## 8: 0.46875 0.0560 42 0.098 .
## 9: 0.53125 0.0440 43 0.15 :(
## 10: 0.59375 -0.0100 45 0.91 :(
## 11: 0.65625 -0.0560 44 0.041 *
## 12: 0.71875 -0.0440 43 0.076 .
## 13: 0.78125 -0.0810 38 0.032 *
## 14: 0.84375 -0.1400 23 0.023 *
## 15: 0.90625 -0.0063 7 0.44 :(
## 16: 0.96875 -0.2400 4 0.2 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 19 0.13 :(
## 3: 35 0.65 :(
## 4: 40 0.36 :(
## 5: 40 0.018 *
## 6: 42 0.19 :(
## 7: 42 0.098 .
## 8: 43 0.15 :(
## 9: 45 0.91 :(
## 10: 44 0.041 *
## 11: 43 0.076 .
## 12: 38 0.032 *
## 13: 23 0.023 *
## 14: 7 0.44 :(
## 15: 4 0.2 :(
## [1] 31.3
## [1] 15.4## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0440 5 0.78 :(
## 3: 0.15625 -0.0730 17 0.057 .
## 4: 0.21875 -0.0190 21 0.61 :(
## 5: 0.28125 0.0190 21 0.42 :(
## 6: 0.34375 0.1100 21 0.023 *
## 7: 0.40625 0.0600 20 0.16 :(
## 8: 0.46875 0.1100 20 0.024 *
## 9: 0.53125 0.1000 19 0.067 .
## 10: 0.59375 0.0880 20 0.18 :(
## 11: 0.65625 0.0051 20 1 :(
## 12: 0.71875 -0.0190 17 0.6 :(
## 13: 0.78125 -0.0560 12 0.25 :(
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 17 0.057 .
## 3: 21 0.61 :(
## 4: 21 0.42 :(
## 5: 21 0.023 *
## 6: 20 0.16 :(
## 7: 20 0.024 *
## 8: 19 0.067 .
## 9: 20 0.18 :(
## 10: 20 1 :(
## 11: 17 0.6 :(
## 12: 12 0.25 :(
## [1] 17.8
## [1] 4.77## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.290 2 1 :(
## 4: 0.21875 0.069 14 0.29 :(
## 5: 0.28125 0.069 19 0.46 :(
## 6: 0.34375 0.076 19 0.32 :(
## 7: 0.40625 0.020 21 0.83 :(
## 8: 0.46875 -0.019 20 0.93 :(
## 9: 0.53125 0.019 20 0.69 :(
## 10: 0.59375 -0.077 20 0.076 .
## 11: 0.65625 -0.160 20 0.0074 **
## 12: 0.71875 -0.056 21 0.088 .
## 13: 0.78125 -0.081 21 0.21 :(
## 14: 0.84375 -0.160 18 0.029 *
## 15: 0.90625 -0.210 2 0.5 :(
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.29 :(
## 3: 19 0.46 :(
## 4: 19 0.32 :(
## 5: 21 0.83 :(
## 6: 20 0.93 :(
## 7: 20 0.69 :(
## 8: 20 0.076 .
## 9: 20 0.0074 **
## 10: 21 0.088 .
## 11: 21 0.21 :(
## 12: 18 0.029 *
## 13: 2 0.5 :(
## [1] 16.7
## [1] 6.77## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 0 NA
## 7: 0.40625 NA 1 NA
## 8: 0.46875 0.1800 2 0.5 :(
## 9: 0.53125 -0.0310 4 0.58 :(
## 10: 0.59375 -0.0270 5 0.78 :(
## 11: 0.65625 -0.0059 4 1 :(
## 12: 0.71875 -0.0520 5 0.62 :(
## 13: 0.78125 -0.0940 5 0.31 :(
## 14: 0.84375 -0.0440 5 0.59 :(
## 15: 0.90625 -0.0062 5 1 :(
## 16: 0.96875 -0.2400 4 0.2 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 0.5 :(
## 2: 4 0.58 :(
## 3: 5 0.78 :(
## 4: 4 1 :(
## 5: 5 0.62 :(
## 6: 5 0.31 :(
## 7: 5 0.59 :(
## 8: 5 1 :(
## 9: 4 0.2 :(
## [1] 4.33
## [1] 1## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0250 39 0.19 :(
## 2: 0.09375 0.0310 49 0.17 :(
## 3: 0.15625 0.0940 48 0.17 :(
## 4: 0.21875 0.0310 36 0.61 :(
## 5: 0.28125 0.0190 35 0.84 :(
## 6: 0.34375 -0.0190 29 0.71 :(
## 7: 0.40625 -0.0062 32 0.9 :(
## 8: 0.46875 -0.1200 32 0.038 *
## 9: 0.53125 -0.1800 29 0.0038 **
## 10: 0.59375 -0.1900 36 0.00056 ***
## 11: 0.65625 -0.1600 34 0.0016 **
## 12: 0.71875 -0.2200 35 7.2e-05 ***
## 13: 0.78125 -0.2600 34 4.7e-06 ***
## 14: 0.84375 -0.2400 41 6.8e-06 ***
## 15: 0.90625 -0.2100 49 3.9e-08 ***
## 16: 0.96875 -0.1000 52 4.3e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 39 0.19 :(
## 2: 49 0.17 :(
## 3: 48 0.17 :(
## 4: 36 0.61 :(
## 5: 35 0.84 :(
## 6: 29 0.71 :(
## 7: 32 0.9 :(
## 8: 32 0.038 *
## 9: 29 0.0038 **
## 10: 36 0.00056 ***
## 11: 34 0.0016 **
## 12: 35 7.2e-05 ***
## 13: 34 4.7e-06 ***
## 14: 41 6.8e-06 ***
## 15: 49 3.9e-08 ***
## 16: 52 4.3e-07 ***
## [1] 38.1
## [1] 7.48
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 8.4e-05 17 1 :(
## 2: 0.09375 -4.4e-02 16 0.36 :(
## 3: 0.15625 9.4e-02 15 0.75 :(
## 4: 0.21875 6.6e-03 8 1 :(
## 5: 0.28125 1.9e-02 12 0.91 :(
## 6: 0.34375 -1.7e-01 10 0.066 .
## 7: 0.40625 -1.6e-01 9 0.12 :(
## 8: 0.46875 -2.2e-01 13 0.017 *
## 9: 0.53125 -2.8e-01 9 0.057 .
## 10: 0.59375 -3.4e-01 12 0.0082 **
## 11: 0.65625 -3.6e-01 11 0.0038 **
## 12: 0.71875 -4.2e-01 12 0.0023 **
## 13: 0.78125 -2.8e-01 11 0.0086 **
## 14: 0.84375 -3.2e-01 13 0.0095 **
## 15: 0.90625 -2.3e-01 15 0.0028 **
## 16: 0.96875 -1.1e-01 17 0.037 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 17 1 :(
## 2: 16 0.36 :(
## 3: 15 0.75 :(
## 4: 8 1 :(
## 5: 12 0.91 :(
## 6: 10 0.066 .
## 7: 9 0.12 :(
## 8: 13 0.017 *
## 9: 9 0.057 .
## 10: 12 0.0082 **
## 11: 11 0.0038 **
## 12: 12 0.0023 **
## 13: 11 0.0086 **
## 14: 13 0.0095 **
## 15: 15 0.0028 **
## 16: 17 0.037 *
## [1] 12.5
## [1] 2.85
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.05200 22 0.11 :(
## 2: 0.09375 0.04300 25 0.18 :(
## 3: 0.15625 -0.00630 23 0.79 :(
## 4: 0.21875 0.00630 20 0.96 :(
## 5: 0.28125 0.00016 15 1 :(
## 6: 0.34375 0.05600 15 0.38 :(
## 7: 0.40625 0.01900 18 0.66 :(
## 8: 0.46875 -0.04400 15 0.71 :(
## 9: 0.53125 -0.08100 15 0.14 :(
## 10: 0.59375 -0.09400 17 0.18 :(
## 11: 0.65625 -0.16000 18 0.059 .
## 12: 0.71875 -0.12000 15 0.049 *
## 13: 0.78125 -0.18000 18 0.0021 **
## 14: 0.84375 -0.24000 20 0.002 **
## 15: 0.90625 -0.19000 24 0.00017 ***
## 16: 0.96875 -0.06500 25 0.00051 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.11 :(
## 2: 25 0.18 :(
## 3: 23 0.79 :(
## 4: 20 0.96 :(
## 5: 15 1 :(
## 6: 15 0.38 :(
## 7: 18 0.66 :(
## 8: 15 0.71 :(
## 9: 15 0.14 :(
## 10: 17 0.18 :(
## 11: 18 0.059 .
## 12: 15 0.049 *
## 13: 18 0.0021 **
## 14: 20 0.002 **
## 15: 24 0.00017 ***
## 16: 25 0.00051 ***
## [1] 19.1
## [1] 3.75
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.160 8 0.1 :(
## 3: 0.15625 0.240 10 0.024 *
## 4: 0.21875 0.070 8 0.44 :(
## 5: 0.28125 0.082 8 0.62 :(
## 6: 0.34375 0.048 4 0.38 :(
## 7: 0.40625 0.160 5 0.28 :(
## 8: 0.46875 NA 4 NA
## 9: 0.53125 -0.180 5 0.058 .
## 10: 0.59375 -0.140 7 0.02 *
## 11: 0.65625 0.044 5 0.78 :(
## 12: 0.71875 -0.094 8 0.29 :(
## 13: 0.78125 -0.280 5 0.054 .
## 14: 0.84375 -0.190 8 0.057 .
## 15: 0.90625 -0.290 10 0.011 *
## 16: 0.96875 -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.1 :(
## 2: 10 0.024 *
## 3: 8 0.44 :(
## 4: 8 0.62 :(
## 5: 4 0.38 :(
## 6: 5 0.28 :(
## 7: 5 0.058 .
## 8: 7 0.02 *
## 9: 5 0.78 :(
## 10: 8 0.29 :(
## 11: 5 0.054 .
## 12: 8 0.057 .
## 13: 10 0.011 *
## 14: 10 0.0059 **
## [1] 7.21
## [1] 2.08## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.094 36 0.0044 **
## 2: 0.09375 0.160 41 3.1e-05 ***
## 3: 0.15625 0.170 42 8.4e-05 ***
## 4: 0.21875 0.260 44 3.2e-06 ***
## 5: 0.28125 0.220 36 0.00012 ***
## 6: 0.34375 0.160 40 5.4e-05 ***
## 7: 0.40625 0.094 44 0.0061 **
## 8: 0.46875 0.031 41 0.038 *
## 9: 0.53125 -0.031 38 0.5 :(
## 10: 0.59375 -0.044 42 0.41 :(
## 11: 0.65625 -0.056 40 0.46 :(
## 12: 0.71875 -0.069 39 0.0097 **
## 13: 0.78125 -0.150 44 0.00022 ***
## 14: 0.84375 -0.230 43 2.1e-07 ***
## 15: 0.90625 -0.260 42 4.7e-07 ***
## 16: 0.96875 -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 36 0.0044 **
## 2: 41 3.1e-05 ***
## 3: 42 8.4e-05 ***
## 4: 44 3.2e-06 ***
## 5: 36 0.00012 ***
## 6: 40 5.4e-05 ***
## 7: 44 0.0061 **
## 8: 41 0.038 *
## 9: 38 0.5 :(
## 10: 42 0.41 :(
## 11: 40 0.46 :(
## 12: 39 0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.050 26 0.071 .
## 2: 0.09375 0.110 26 0.007 **
## 3: 0.15625 0.120 25 0.015 *
## 4: 0.21875 0.210 24 0.0013 **
## 5: 0.28125 0.150 18 0.074 .
## 6: 0.34375 0.160 21 0.046 *
## 7: 0.40625 0.120 22 0.04 *
## 8: 0.46875 0.031 20 0.44 :(
## 9: 0.53125 -0.031 19 0.5 :(
## 10: 0.59375 -0.094 22 0.12 :(
## 11: 0.65625 -0.073 17 0.42 :(
## 12: 0.71875 -0.100 19 0.034 *
## 13: 0.78125 -0.130 22 0.013 *
## 14: 0.84375 -0.240 19 0.00097 ***
## 15: 0.90625 -0.310 16 0.0029 **
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 26 0.071 .
## 2: 26 0.007 **
## 3: 25 0.015 *
## 4: 24 0.0013 **
## 5: 18 0.074 .
## 6: 21 0.046 *
## 7: 22 0.04 *
## 8: 20 0.44 :(
## 9: 19 0.5 :(
## 10: 22 0.12 :(
## 11: 17 0.42 :(
## 12: 19 0.034 *
## 13: 22 0.013 *
## 14: 19 0.00097 ***
## 15: 16 0.0029 **
## [1] 21.1
## [1] 3.17## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.220 10 0.032 *
## 2: 0.09375 0.360 14 0.0019 **
## 3: 0.15625 0.340 14 0.0015 **
## 4: 0.21875 0.280 16 0.0028 **
## 5: 0.28125 0.220 13 0.0014 **
## 6: 0.34375 0.160 13 0.0018 **
## 7: 0.40625 0.094 15 0.37 :(
## 8: 0.46875 0.031 13 0.12 :(
## 9: 0.53125 -0.031 12 0.49 :(
## 10: 0.59375 0.044 13 0.16 :(
## 11: 0.65625 -0.110 15 0.11 :(
## 12: 0.71875 -0.019 14 0.22 :(
## 13: 0.78125 -0.210 15 0.0055 **
## 14: 0.84375 -0.240 16 0.00068 ***
## 15: 0.90625 -0.240 16 0.0014 **
## 16: 0.96875 -0.340 16 0.00052 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.032 *
## 2: 14 0.0019 **
## 3: 14 0.0015 **
## 4: 16 0.0028 **
## 5: 13 0.0014 **
## 6: 13 0.0018 **
## 7: 15 0.37 :(
## 8: 13 0.12 :(
## 9: 12 0.49 :(
## 10: 13 0.16 :(
## 11: 15 0.11 :(
## 12: 14 0.22 :(
## 13: 15 0.0055 **
## 14: 16 0.00068 ***
## 15: 16 0.0014 **
## 16: 16 0.00052 ***
## [1] 14.1
## [1] 1.69
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.270 4 0.12 :(
## 5: 0.28125 0.340 5 0.1 :(
## 6: 0.34375 0.210 6 0.056 .
## 7: 0.40625 0.230 7 0.15 :(
## 8: 0.46875 0.170 8 0.14 :(
## 9: 0.53125 0.019 7 0.8 :(
## 10: 0.59375 -0.077 7 0.35 :(
## 11: 0.65625 0.094 8 0.29 :(
## 12: 0.71875 -0.069 6 0.53 :(
## 13: 0.78125 -0.068 7 0.67 :(
## 14: 0.84375 -0.220 8 0.041 *
## 15: 0.90625 -0.260 10 0.014 *
## 16: 0.96875 -0.370 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 4 0.12 :(
## 2: 5 0.1 :(
## 3: 6 0.056 .
## 4: 7 0.15 :(
## 5: 8 0.14 :(
## 6: 7 0.8 :(
## 7: 7 0.35 :(
## 8: 8 0.29 :(
## 9: 6 0.53 :(
## 10: 7 0.67 :(
## 11: 8 0.041 *
## 12: 10 0.014 *
## 13: 10 0.0059 **
## [1] 7.15
## [1] 1.72## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.75587 -0.18208 0.01722 0.17996 0.67980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07834 0.02339 3.350 0.00083 ***
## timeNorm 0.01356 0.02393 0.567 0.57104
## obj.diff -0.19206 0.03147 -6.103 1.35e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05882497)
##
## Null deviance: 82.357 on 1362 degrees of freedom
## Residual deviance: 80.002 on 1360 degrees of freedom
## AIC: 11.387
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81067 -0.18405 -0.03539 0.21809 0.81473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05368 0.01828 2.936 0.00338 **
## timeNorm 0.05164 0.02428 2.127 0.03362 *
## obj.diff -0.29020 0.01884 -15.405 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06914682)
##
## Null deviance: 120.86 on 1507 degrees of freedom
## Residual deviance: 104.07 on 1505 degrees of freedom
## AIC: 255.86
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73430 -0.20594 -0.01949 0.19850 0.71398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21759 0.02001 10.88 <2e-16 ***
## timeNorm 0.05914 0.02495 2.37 0.0179 *
## obj.diff -0.53045 0.02119 -25.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06995631)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 107.31 on 1534 degrees of freedom
## AIC: 278.57
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5414894 0.5916709 -0.041797918 94 0.16 :(
## 2: 4.5 0.5347518 0.5750233 -0.031880263 141 0.16 :(
## 3: 7.5 0.5085106 0.5313589 -0.018533292 141 0.41 :(
## 4: 10.5 0.5404255 0.5341000 0.017669339 141 0.43 :(
## 5: 13.5 0.5085106 0.5167958 -0.006673180 141 0.77 :(
## 6: 16.5 0.5276596 0.5259445 0.002686940 141 0.9 :(
## 7: 19.5 0.4971631 0.5307814 -0.035626571 141 0.081 .
## 8: 22.5 0.4737589 0.4890926 -0.014471502 141 0.5 :(
## 9: 25.5 0.4758865 0.4723221 0.005367814 141 0.81 :(
## 10: 28.5 0.4574468 0.4526413 0.002528689 141 0.88 :(
## time error.diff shapes
## 1: 1.5 -0.041797918 16
## 2: 4.5 -0.031880263 16
## 3: 7.5 -0.018533292 16
## 4: 10.5 0.017669339 16
## 5: 13.5 -0.006673180 16
## 6: 16.5 0.002686940 16
## 7: 19.5 -0.035626571 16
## 8: 22.5 -0.014471502 16
## 9: 25.5 0.005367814 16
## 10: 28.5 0.002528689 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4682692 0.5971846 -0.13701885 104 2.8e-05 ***
## 2: 4.5 0.5076923 0.6238752 -0.09911341 156 1.4e-07 ***
## 3: 7.5 0.4660256 0.5314762 -0.06766846 156 0.0022 **
## 4: 10.5 0.5160256 0.5982780 -0.07895358 156 8.9e-05 ***
## 5: 13.5 0.4679487 0.5755768 -0.09400514 156 2.4e-07 ***
## 6: 16.5 0.4211538 0.5249293 -0.10790217 156 2.1e-06 ***
## 7: 19.5 0.4794872 0.5492939 -0.05610758 156 0.001 **
## 8: 22.5 0.4993590 0.5710919 -0.05957949 156 0.0025 **
## 9: 25.5 0.5474359 0.5949924 -0.03147437 156 0.069 .
## 10: 28.5 0.4993590 0.5713447 -0.06615746 156 0.0014 **
## time error.diff shapes
## 1: 1.5 -0.13701885 24
## 2: 4.5 -0.09911341 24
## 3: 7.5 -0.06766846 24
## 4: 10.5 -0.07895358 24
## 5: 13.5 -0.09400514 24
## 6: 16.5 -0.10790217 24
## 7: 19.5 -0.05610758 24
## 8: 22.5 -0.05957949 24
## 9: 25.5 -0.03147437 16
## 10: 28.5 -0.06615746 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4415094 0.6007697 -1.658770e-01 106 3.8e-06 ***
## 2: 4.5 0.5119497 0.6324837 -1.343840e-01 159 4.2e-06 ***
## 3: 7.5 0.5100629 0.5479813 -4.895619e-02 159 0.069 .
## 4: 10.5 0.5220126 0.5177334 2.196993e-03 159 0.93 :(
## 5: 13.5 0.5169811 0.5303606 -2.035258e-02 159 0.43 :(
## 6: 16.5 0.5100629 0.5026471 2.226322e-05 159 1 :(
## 7: 19.5 0.4584906 0.4514766 -3.401739e-03 159 0.87 :(
## 8: 22.5 0.4226415 0.4287566 -1.335901e-02 159 0.6 :(
## 9: 25.5 0.4584906 0.3964332 6.936761e-02 159 0.013 *
## 10: 28.5 0.4446541 0.3652666 6.326623e-02 159 0.012 *
## time error.diff shapes
## 1: 1.5 -1.658770e-01 24
## 2: 4.5 -1.343840e-01 24
## 3: 7.5 -4.895619e-02 16
## 4: 10.5 2.196993e-03 16
## 5: 13.5 -2.035258e-02 16
## 6: 16.5 2.226322e-05 16
## 7: 19.5 -3.401739e-03 16
## 8: 22.5 -1.335901e-02 16
## 9: 25.5 6.936761e-02 24
## 10: 28.5 6.326623e-02 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77507 -0.18892 -0.04563 0.23835 0.56199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25898 0.03499 7.401 3.77e-13 ***
## timeNorm 0.10685 0.03407 3.137 0.00178 **
## obj.diff -0.58560 0.03530 -16.589 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06412537)
##
## Null deviance: 65.914 on 724 degrees of freedom
## Residual deviance: 46.299 on 722 degrees of freedom
## AIC: 70.941
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7610 -0.2198 0.0094 0.2181 0.7586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16969 0.01890 8.977 <2e-16 ***
## timeNorm 0.03591 0.02247 1.598 0.11
## obj.diff -0.39843 0.02113 -18.853 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07075553)
##
## Null deviance: 155.26 on 1826 degrees of freedom
## Residual deviance: 129.06 on 1824 degrees of freedom
## AIC: 350.94
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78066 -0.17153 -0.03285 0.20522 0.74779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09909 0.01663 5.958 3.04e-09 ***
## timeNorm 0.03821 0.02165 1.765 0.0777 .
## obj.diff -0.31369 0.02075 -15.118 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06572702)
##
## Null deviance: 138.07 on 1855 degrees of freedom
## Residual deviance: 121.79 on 1853 degrees of freedom
## AIC: 219.61
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5600000 0.7780956 -0.22391727 50 7.2e-06 ***
## 2: 4.5 0.5666667 0.7843893 -0.23287009 75 2e-07 ***
## 3: 7.5 0.6200000 0.7854507 -0.18214977 75 7.4e-06 ***
## 4: 10.5 0.6520000 0.7446165 -0.09638441 75 0.011 *
## 5: 13.5 0.6520000 0.7586383 -0.11964999 75 0.00099 ***
## 6: 16.5 0.6173333 0.7249928 -0.11941562 75 0.0022 **
## 7: 19.5 0.6426667 0.7075656 -0.07119700 75 0.029 *
## 8: 22.5 0.6266667 0.7271200 -0.09999538 75 0.012 *
## 9: 25.5 0.6120000 0.6846440 -0.06660751 75 0.11 :(
## 10: 28.5 0.6346667 0.6657753 -0.02169353 75 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.22391727 24
## 2: 4.5 -0.23287009 24
## 3: 7.5 -0.18214977 24
## 4: 10.5 -0.09638441 24
## 5: 13.5 -0.11964999 24
## 6: 16.5 -0.11941562 24
## 7: 19.5 -0.07119700 24
## 8: 22.5 -0.09999538 24
## 9: 25.5 -0.06660751 16
## 10: 28.5 -0.02169353 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5150794 0.6189371 -0.106458111 126 0.00044 ***
## 2: 4.5 0.5798942 0.6776920 -0.094630375 189 9.6e-06 ***
## 3: 7.5 0.5195767 0.5252645 -0.012513224 189 0.57 :(
## 4: 10.5 0.5523810 0.5738743 -0.016145673 189 0.45 :(
## 5: 13.5 0.5296296 0.5716864 -0.041794279 189 0.031 *
## 6: 16.5 0.5232804 0.5553508 -0.037051765 189 0.091 .
## 7: 19.5 0.4984127 0.5539123 -0.056002923 189 0.0049 **
## 8: 22.5 0.4968254 0.5281654 -0.039304341 189 0.085 .
## 9: 25.5 0.5439153 0.5315052 0.009606146 189 0.67 :(
## 10: 28.5 0.5084656 0.5060812 -0.006892386 189 0.72 :(
## time error.diff shapes
## 1: 1.5 -0.106458111 24
## 2: 4.5 -0.094630375 24
## 3: 7.5 -0.012513224 16
## 4: 10.5 -0.016145673 16
## 5: 13.5 -0.041794279 24
## 6: 16.5 -0.037051765 16
## 7: 19.5 -0.056002923 24
## 8: 22.5 -0.039304341 16
## 9: 25.5 0.009606146 16
## 10: 28.5 -0.006892386 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4179688 0.5040234 -0.0786571251 128 0.0047 **
## 2: 4.5 0.4369792 0.4794519 -0.0425210394 192 0.033 *
## 3: 7.5 0.4208333 0.4519642 -0.0284750656 192 0.15 :(
## 4: 10.5 0.4500000 0.4513051 0.0000955907 192 1 :(
## 5: 13.5 0.4057292 0.4272861 -0.0215126349 192 0.3 :(
## 6: 16.5 0.3958333 0.3991265 -0.0083645826 192 0.71 :(
## 7: 19.5 0.3927083 0.3883227 -0.0046030177 192 0.79 :(
## 8: 22.5 0.3697917 0.3743096 -0.0078604043 192 0.64 :(
## 9: 25.5 0.3994792 0.3679496 0.0271632447 192 0.13 :(
## 10: 28.5 0.3614583 0.3408703 0.0050885344 192 0.77 :(
## time error.diff shapes
## 1: 1.5 -0.0786571251 24
## 2: 4.5 -0.0425210394 24
## 3: 7.5 -0.0284750656 16
## 4: 10.5 0.0000955907 16
## 5: 13.5 -0.0215126349 16
## 6: 16.5 -0.0083645826 16
## 7: 19.5 -0.0046030177 16
## 8: 22.5 -0.0078604043 16
## 9: 25.5 0.0271632447 16
## 10: 28.5 0.0050885344 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72641 -0.18104 0.07896 0.17901 0.32506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17441 0.11392 1.531 0.1280
## timeNorm 0.05973 0.06311 0.946 0.3455
## obj.diff -0.34358 0.13212 -2.600 0.0103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04409487)
##
## Null deviance: 6.6352 on 144 degrees of freedom
## Residual deviance: 6.2615 on 142 degrees of freedom
## AIC: -36.144
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.7000000 0.8422160 -0.1313282577 10 0.084 .
## 2: 4.5 0.7200000 0.8045511 -0.0795853843 15 0.49 :(
## 3: 7.5 0.6933333 0.7637930 -0.0692528767 15 0.25 :(
## 4: 10.5 0.7200000 0.7894410 -0.0625540262 15 0.36 :(
## 5: 13.5 0.7000000 0.8006171 -0.1084499111 15 0.055 .
## 6: 16.5 0.7200000 0.7661172 -0.0140493196 15 0.8 :(
## 7: 19.5 0.7466667 0.7396280 0.0120888681 15 0.8 :(
## 8: 22.5 0.7333333 0.7489324 -0.0006995685 15 1 :(
## 9: 25.5 0.7533333 0.8163298 -0.0314486706 15 0.6 :(
## 10: 28.5 0.6866667 0.7440259 -0.0101905199 15 0.85 :(
## time error.diff shapes
## 1: 1.5 -0.1313282577 16
## 2: 4.5 -0.0795853843 16
## 3: 7.5 -0.0692528767 16
## 4: 10.5 -0.0625540262 16
## 5: 13.5 -0.1084499111 16
## 6: 16.5 -0.0140493196 16
## 7: 19.5 0.0120888681 16
## 8: 22.5 -0.0006995685 16
## 9: 25.5 -0.0314486706 16
## 10: 28.5 -0.0101905199 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7008 -0.1756 0.0104 0.2007 0.6764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.129171 0.042821 3.017 0.00266 **
## timeNorm -0.001606 0.039220 -0.041 0.96736
## obj.diff -0.312573 0.058219 -5.369 1.13e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06994394)
##
## Null deviance: 44.480 on 608 degrees of freedom
## Residual deviance: 42.386 on 606 degrees of freedom
## AIC: 113.28
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5214286 0.6226413 -0.094272559 42 0.054 .
## 2: 4.5 0.5476190 0.6192837 -0.065077266 63 0.076 .
## 3: 7.5 0.5222222 0.5488374 -0.021946708 63 0.54 :(
## 4: 10.5 0.5269841 0.5633358 -0.017849848 63 0.63 :(
## 5: 13.5 0.5365079 0.5457213 -0.003702683 63 0.93 :(
## 6: 16.5 0.5285714 0.5497442 -0.024443456 63 0.5 :(
## 7: 19.5 0.4698413 0.5571074 -0.092288817 63 0.0066 **
## 8: 22.5 0.4412698 0.5026156 -0.066421840 63 0.067 .
## 9: 25.5 0.4777778 0.4906858 -0.015672991 63 0.67 :(
## 10: 28.5 0.4777778 0.4965908 -0.024207547 63 0.42 :(
## time error.diff shapes
## 1: 1.5 -0.094272559 16
## 2: 4.5 -0.065077266 16
## 3: 7.5 -0.021946708 16
## 4: 10.5 -0.017849848 16
## 5: 13.5 -0.003702683 16
## 6: 16.5 -0.024443456 16
## 7: 19.5 -0.092288817 24
## 8: 22.5 -0.066421840 16
## 9: 25.5 -0.015672991 16
## 10: 28.5 -0.024207547 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.66104 -0.16469 -0.00053 0.17110 0.56752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003495 0.031165 0.112 0.911
## timeNorm 0.030048 0.032813 0.916 0.360
## obj.diff 0.014011 0.048027 0.292 0.771
##
## (Dispersion parameter for gaussian family taken to be 0.04854566)
##
## Null deviance: 29.460 on 608 degrees of freedom
## Residual deviance: 29.419 on 606 degrees of freedom
## AIC: -109.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5238095 0.5010468 0.029289623 42 0.44 :(
## 2: 4.5 0.4777778 0.4761135 0.006623743 63 0.82 :(
## 3: 7.5 0.4507937 0.4585390 -0.004803129 63 0.9 :(
## 4: 10.5 0.5111111 0.4440686 0.079755086 63 0.014 *
## 5: 13.5 0.4349206 0.4202938 0.019074305 63 0.57 :(
## 6: 16.5 0.4809524 0.4449609 0.036542116 63 0.21 :(
## 7: 19.5 0.4650794 0.4547299 0.003697701 63 0.89 :(
## 8: 22.5 0.4444444 0.4137030 0.029021771 63 0.27 :(
## 9: 25.5 0.4079365 0.3720518 0.034615081 63 0.19 :(
## 10: 28.5 0.3825397 0.3393145 0.035297116 63 0.18 :(
## time error.diff shapes
## 1: 1.5 0.029289623 16
## 2: 4.5 0.006623743 16
## 3: 7.5 -0.004803129 16
## 4: 10.5 0.079755086 24
## 5: 13.5 0.019074305 16
## 6: 16.5 0.036542116 16
## 7: 19.5 0.003697701 16
## 8: 22.5 0.029021771 16
## 9: 25.5 0.034615081 16
## 10: 28.5 0.035297116 16
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74330 -0.20419 -0.03214 0.20665 0.62427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21074 0.04485 4.699 4.07e-06 ***
## timeNorm 0.05057 0.05281 0.958 0.339
## obj.diff -0.51446 0.04476 -11.495 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06277817)
##
## Null deviance: 26.452 on 289 degrees of freedom
## Residual deviance: 18.017 on 287 degrees of freedom
## AIC: 25.206
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5200000 0.6518365 -0.15393554 20 0.07 .
## 2: 4.5 0.5233333 0.6787481 -0.15856818 30 0.0099 **
## 3: 7.5 0.5600000 0.7244106 -0.17547768 30 0.004 **
## 4: 10.5 0.6166667 0.7076965 -0.09632428 30 0.1 :(
## 5: 13.5 0.6300000 0.7376481 -0.09834264 30 0.047 *
## 6: 16.5 0.5033333 0.6323586 -0.17289582 30 0.025 *
## 7: 19.5 0.5666667 0.6717296 -0.14495973 30 0.064 .
## 8: 22.5 0.6766667 0.7251853 -0.04231065 30 0.56 :(
## 9: 25.5 0.5200000 0.6330884 -0.10243048 30 0.088 .
## 10: 28.5 0.5400000 0.6152669 -0.06054258 30 0.32 :(
## time error.diff shapes
## 1: 1.5 -0.15393554 16
## 2: 4.5 -0.15856818 24
## 3: 7.5 -0.17547768 24
## 4: 10.5 -0.09632428 16
## 5: 13.5 -0.09834264 24
## 6: 16.5 -0.17289582 24
## 7: 19.5 -0.14495973 16
## 8: 22.5 -0.04231065 16
## 9: 25.5 -0.10243048 16
## 10: 28.5 -0.06054258 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73906 -0.18345 0.02625 0.17355 0.80187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05584 0.02637 2.118 0.0345 *
## timeNorm 0.05633 0.03465 1.626 0.1045
## obj.diff -0.23667 0.02721 -8.699 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06768238)
##
## Null deviance: 54.202 on 724 degrees of freedom
## Residual deviance: 48.867 on 722 degrees of freedom
## AIC: 110.08
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5280000 0.6066168 -0.086792974 50 0.045 *
## 2: 4.5 0.5920000 0.6783086 -0.060722899 75 0.0014 **
## 3: 7.5 0.4840000 0.4910626 -0.019142965 75 0.56 :(
## 4: 10.5 0.5333333 0.6197591 -0.078082680 75 0.014 *
## 5: 13.5 0.4946667 0.5780738 -0.070454812 75 0.0044 **
## 6: 16.5 0.4706667 0.5383262 -0.062513590 75 0.057 .
## 7: 19.5 0.5160000 0.5458392 -0.010005193 75 0.64 :(
## 8: 22.5 0.5066667 0.5583782 -0.041370638 75 0.081 .
## 9: 25.5 0.6066667 0.6123948 -0.002570066 75 0.89 :(
## 10: 28.5 0.5653333 0.5950078 -0.031782736 75 0.18 :(
## time error.diff shapes
## 1: 1.5 -0.086792974 24
## 2: 4.5 -0.060722899 24
## 3: 7.5 -0.019142965 16
## 4: 10.5 -0.078082680 24
## 5: 13.5 -0.070454812 24
## 6: 16.5 -0.062513590 16
## 7: 19.5 -0.010005193 16
## 8: 22.5 -0.041370638 16
## 9: 25.5 -0.002570066 16
## 10: 28.5 -0.031782736 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70803 -0.14336 -0.04869 0.22741 0.79339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006508 0.029732 0.219 0.827
## timeNorm 0.039932 0.041845 0.954 0.340
## obj.diff -0.293722 0.031904 -9.206 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06714128)
##
## Null deviance: 38.646 on 492 degrees of freedom
## Residual deviance: 32.899 on 490 degrees of freedom
## AIC: 72.493
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3500000 0.5511656 -0.21685432 34 0.00084 ***
## 2: 4.5 0.3745098 0.5115479 -0.12017012 51 0.00075 ***
## 3: 7.5 0.3843137 0.4774172 -0.07698230 51 0.019 *
## 4: 10.5 0.4313725 0.5023243 -0.07802084 51 0.012 *
## 5: 13.5 0.3333333 0.4765687 -0.14046332 51 0.00023 ***
## 6: 16.5 0.3000000 0.4420343 -0.13435034 51 9.1e-05 ***
## 7: 19.5 0.3745098 0.4823532 -0.08316170 51 0.00086 ***
## 8: 22.5 0.3843137 0.4991454 -0.09891349 51 0.012 *
## 9: 25.5 0.4764706 0.5469911 -0.04107961 51 0.12 :(
## 10: 28.5 0.3784314 0.5107095 -0.11359359 51 5e-04 ***
## time error.diff shapes
## 1: 1.5 -0.21685432 24
## 2: 4.5 -0.12017012 24
## 3: 7.5 -0.07698230 24
## 4: 10.5 -0.07802084 24
## 5: 13.5 -0.14046332 24
## 6: 16.5 -0.13435034 24
## 7: 19.5 -0.08316170 24
## 8: 22.5 -0.09891349 24
## 9: 25.5 -0.04107961 16
## 10: 28.5 -0.11359359 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72111 -0.14638 -0.09293 0.27600 0.48354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.39506 0.06828 5.786 1.89e-08 ***
## timeNorm 0.14360 0.05739 2.502 0.0129 *
## obj.diff -0.80061 0.06610 -12.112 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06790636)
##
## Null deviance: 32.175 on 289 degrees of freedom
## Residual deviance: 19.489 on 287 degrees of freedom
## AIC: 47.977
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5300000 0.8722945 -0.35800592 20 1.9e-05 ***
## 2: 4.5 0.5333333 0.8799494 -0.42037484 30 1.6e-07 ***
## 3: 7.5 0.6433333 0.8573196 -0.24520604 30 0.0013 **
## 4: 10.5 0.6533333 0.7591243 -0.11122511 30 0.1 :(
## 5: 13.5 0.6500000 0.7586393 -0.15551063 30 0.088 .
## 6: 16.5 0.6800000 0.7970647 -0.12033241 30 0.067 .
## 7: 19.5 0.6666667 0.7273704 -0.04953337 30 0.23 :(
## 8: 22.5 0.5233333 0.7181484 -0.19921758 30 0.0022 **
## 9: 25.5 0.6333333 0.6703566 -0.02323196 30 0.84 :(
## 10: 28.5 0.7033333 0.6771584 0.01914086 30 0.75 :(
## time error.diff shapes
## 1: 1.5 -0.35800592 24
## 2: 4.5 -0.42037484 24
## 3: 7.5 -0.24520604 24
## 4: 10.5 -0.11122511 16
## 5: 13.5 -0.15551063 16
## 6: 16.5 -0.12033241 16
## 7: 19.5 -0.04953337 16
## 8: 22.5 -0.19921758 24
## 9: 25.5 -0.02323196 16
## 10: 28.5 0.01914086 16## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6621 -0.1253 -0.0197 0.1346 0.5523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43590 0.03435 12.689 <2e-16 ***
## timeNorm -0.02641 0.04010 -0.659 0.51
## obj.diff -0.76579 0.03613 -21.197 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05809432)
##
## Null deviance: 55.788 on 492 degrees of freedom
## Residual deviance: 28.466 on 490 degrees of freedom
## AIC: 1.1403
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4882353 0.6324795 -0.143920764 34 0.022 *
## 2: 4.5 0.6019608 0.7489367 -0.164740277 51 0.0021 **
## 3: 7.5 0.5686275 0.5464421 0.006702857 51 0.89 :(
## 4: 10.5 0.6117647 0.5194147 0.088569406 51 0.11 :(
## 5: 13.5 0.5725490 0.5943676 -0.031921449 51 0.49 :(
## 6: 16.5 0.5941176 0.5873127 -0.008698008 51 0.82 :(
## 7: 19.5 0.5078431 0.5618376 -0.061219558 51 0.18 :(
## 8: 22.5 0.5509804 0.5152963 0.034648056 51 0.55 :(
## 9: 25.5 0.5333333 0.4629739 0.074885277 51 0.13 :(
## 10: 28.5 0.4627451 0.3870300 0.082569412 51 0.13 :(
## time error.diff shapes
## 1: 1.5 -0.143920764 24
## 2: 4.5 -0.164740277 24
## 3: 7.5 0.006702857 16
## 4: 10.5 0.088569406 16
## 5: 13.5 -0.031921449 16
## 6: 16.5 -0.008698008 16
## 7: 19.5 -0.061219558 16
## 8: 22.5 0.034648056 16
## 9: 25.5 0.074885277 16
## 10: 28.5 0.082569412 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65879 -0.19764 -0.04055 0.21062 0.72428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13031 0.02751 4.737 2.59e-06 ***
## timeNorm 0.06201 0.03610 1.718 0.0863 .
## obj.diff -0.38121 0.03503 -10.881 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06928019)
##
## Null deviance: 62.177 on 753 degrees of freedom
## Residual deviance: 52.029 on 751 degrees of freedom
## AIC: 131.88
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3769231 0.4756038 -0.100098761 52 0.041 *
## 2: 4.5 0.4448718 0.4611623 -0.028101324 78 0.43 :(
## 3: 7.5 0.4205128 0.4300114 -0.017591275 78 0.61 :(
## 4: 10.5 0.4128205 0.4237913 -0.009060319 78 0.8 :(
## 5: 13.5 0.4294872 0.4007105 0.033286086 78 0.46 :(
## 6: 16.5 0.3897436 0.3340513 0.051497071 78 0.13 :(
## 7: 19.5 0.3461538 0.2732045 0.067072182 78 0.075 .
## 8: 22.5 0.3000000 0.2608684 0.012950063 78 0.6 :(
## 9: 25.5 0.3423077 0.2475707 0.092260597 78 0.012 *
## 10: 28.5 0.3333333 0.2310782 0.075326505 78 0.051 .
## time error.diff shapes
## 1: 1.5 -0.100098761 24
## 2: 4.5 -0.028101324 16
## 3: 7.5 -0.017591275 16
## 4: 10.5 -0.009060319 16
## 5: 13.5 0.033286086 16
## 6: 16.5 0.051497071 16
## 7: 19.5 0.067072182 16
## 8: 22.5 0.012950063 16
## 9: 25.5 0.092260597 24
## 10: 28.5 0.075326505 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81618 -0.17452 0.01839 0.17880 0.72382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11059 0.06523 1.696 0.0902 .
## est.confidence.norm -0.24949 0.12971 -1.923 0.0546 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06034802)
##
## Null deviance: 82.357 on 1362 degrees of freedom
## Residual deviance: 82.134 on 1361 degrees of freedom
## AIC: 45.229
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.90773 -0.16814 0.03046 0.12705 0.96840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10609 0.09271 1.144 0.2527
## est.confidence.norm -0.38198 0.18469 -2.068 0.0388 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08002482)
##
## Null deviance: 120.86 on 1507 degrees of freedom
## Residual deviance: 120.52 on 1506 degrees of freedom
## AIC: 475.19
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.00112 -0.21250 -0.01755 0.22285 0.89880
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15450 0.09022 1.712 0.0870 .
## est.confidence.norm -0.33422 0.17948 -1.862 0.0628 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1017497)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 156.19 on 1535 degrees of freedom
## AIC: 853.4
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1106.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4387 -0.6304 0.0084 0.6163 3.3755
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.009937 0.09968
## Residual 0.072865 0.26994
## Number of obs: 4408, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.13266 0.04725 2935.00000 2.807 0.005027 **
## est.confidence.norm -0.34875 0.09018 4364.00000 -3.867 0.000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.955
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -529.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9110 -0.6792 -0.0238 0.7010 3.1464
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02533 0.1592
## Residual 0.03554 0.1885
## Number of obs: 1363, groups: IDjoueur, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.15464 0.05592 813.80000 2.765 0.005818 **
## est.confidence.norm -0.33754 0.10119 1319.20000 -3.336 0.000875 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.905
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 349.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1608 -0.6563 0.0653 0.5634 3.8175
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01094 0.1046
## Residual 0.06928 0.2632
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.10105 0.08902 1504.20000 1.135 0.2565
## est.confidence.norm -0.37190 0.17498 1475.90000 -2.125 0.0337 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.984
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 675.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9862 -0.6557 -0.0570 0.6629 3.1899
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01744 0.1321
## Residual 0.08468 0.2910
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.24536 0.08514 1511.50000 2.882 0.00401 **
## est.confidence.norm -0.51572 0.16550 1493.20000 -3.116 0.00187 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.973
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0243, p-value = 0.04294
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1276282
##
## [1] "pvg.on.error -0.13 0.043 *"Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.4891, p-value = 0.01281
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1547884
##
## [1] "pbg.on.error -0.15 0.013 *"In game level and subjective difficulty error
## [1] "niveau.group.on.error.l 0.097 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5696, p-value = 0.1165
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08637111
##
## Kendall's rank correlation tau
##
## data: Y and X
## T = 523, p-value = 0.7565
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03237743
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0258, p-value = 0.305
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09803922
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8563, p-value = 0.06341
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1756168
##
## [1] "niveau.group.on.error.l 0.18 0.063 ."Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.3506, p-value = 0.0008062
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2247349
##
## [1] "sexe.on.error 0.22 0.00081 ***"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6384, p-value = 0.1013
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1993259
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1555, p-value = 0.03112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2488067
##
## [1] "sexe.on.error.s 0.25 0.031 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.966, p-value = 0.0493
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2246958
##
## [1] "sexe.on.error.l 0.22 0.049 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3490, p-value = 0.0008119
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01773350 0.09107901
## sample estimates:
## difference in location
## 0.05579481
##
## [1] "sexe.on.error.2 0.056 0.00081 *** mean(A): -0.057 mean(B): -0.0023"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 329, p-value = 0.1041
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.006855887 0.109293413
## sample estimates:
## difference in location
## 0.04767336
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 408, p-value = 0.0309
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.005635925 0.122074625
## sample estimates:
## difference in location
## 0.06400045
##
## [1] "sexe.on.error.s.2 0.064 0.031 * mean(A): -0.059 mean(B): 0.004"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 429, p-value = 0.04975
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.0002747141 0.1116709810
## sample estimates:
## difference in location
## 0.05413278
##
## [1] "sexe.on.error.l.2 0.054 0.05 . mean(A): -0.06 mean(B): -0.0073"Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.52339, p-value = 0.6007
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03130149## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.39687, p-value = 0.6915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04335873
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.72909, p-value = 0.4659
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07499906
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.4961, p-value = 0.6198
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05055382Self efficacy and subjective difficulty error
## Warning: Removed 74 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.4234, p-value = 0.0006184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2763483
##
## [1] "self.eff.on.error -0.28 0.00062 ***"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 23 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.9464, p-value = 0.05161
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2900611
##
## [1] "self.eff.on.error -0.29 0.052 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.8079, p-value = 0.07063
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2470292
##
## [1] "self.eff.on.error -0.25 0.071 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0636, p-value = 0.03905
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2944232
##
## [1] "self.eff.on.error -0.29 0.039 *"